Bertrand Lubac

Uncertainties of optical parameters and their propagations in an analytical ocean color inversion algorithm.

Following the theory of error propagation, we developed analytical functions to illustrate and evaluate the uncertainties of inherent optical properties (IOPs) derived by the quasi-analytical algorithm (QAA). In particular, we evaluated the effects of uncertainties of these optical parameters on the inverted IOPs: the absorption coefficient at the reference wavelength, the extrapolation of particle backscattering coefficient, and the spectral ratios of absorption coefficients of phytoplankton and detritus/gelbstoff, respectively. With a systematically simulated data set (46,200 points), we found that the relative uncertainty of QAA-derived total absorption coefficients in the blue-green wavelengths is generally within +/-10% for oceanic waters. The results of this study not only establish theoretical bases to evaluate and understand the effects of the various variables on IOPs derived from remote-sensing reflectance, but also lay the groundwork to analytically estimate uncertainties of these IOPs for each pixel. These are required and important steps for the generation of quality maps of IOP products derived from satellite ocean color remote sensing.

Effect of inherent optical properties variability on the chlorophyll retrieval from ocean color remote sensing: an in situ approach

The impact of the inherent optical properties (IOP) variability on the chlorophyll, Chl, retrieval from ocean color remote sensing algorithms is analyzed from an in situ data set covering a large dynamic range. The effect of the variability of the specific phytoplankton absorption coefficient, a(phy)/Chl, specific particulate backscattering coefficient, b(bp)/Chl, and colored detrital matter absorption to non-water absorption ratio, a(cdm)/a(nw), on the performance of standard operational algorithms is examined. This study confirms that empirical algorithms are highly dependent on the specifics IOP values (especially b(bp)/Chl and a(cdm)/a(nw)): Chl is over-estimated in waters with specific IOP values higher than averaged values, and vice versa. These results clearly indicate the necessity to account for the influence of the specific IOP variability in Chl retrieval algorithms.

An inherent-optical-property-centered approach to correct the angular effects in water-leaving radiance

Remote-sensing reflectance (R(rs)), which is defined as the ratio of water-leaving radiance (L(w)) to downwelling irradiance just above the surface (E(d)(0⁺)), varies with both water constituents (including bottom properties of optically-shallow waters) and angular geometry. L(w) is commonly measured in the field or by satellite sensors at convenient angles, while E(d)(0⁺) can be measured in the field or estimated based on atmospheric properties. To isolate the variations of R(rs) (or L(w)) resulting from a change of water constituents, the angular effects of R(rs) (or L(w)) need to be removed. This is also a necessity for the calibration and validation of satellite ocean color measurements. To reach this objective, for optically-deep waters where bottom contribution is negligible, we present a system centered on waters inherent optical properties (IOPs). It can be used to derive IOPs from angular Rrs and offers an alternative to the system centered on the concentration of chlorophyll. This system is applicable to oceanic and coastal waters as well as to multiband and hyperspectral sensors. This IOP-centered system is applied to both numerically simulated data and in situ measurements to test and evaluate its performance. The good results obtained suggest that the system can be applied to angular R(rs) to retrieve IOPs and to remove the angular variation of R(rs).

Hyperspectral and multispectral ocean color inversions to detect Phaeocystis globosa blooms in coastal waters

Identification of phytoplankton groups from space is essential to map and monitor algal blooms in coastal waters, but remains a challenge due to the presence of suspended sediments and dissolved organic matter which interfere with phytoplankton signal. On the basis of field measurements of remote sensing reflectance (Rrs(λ)), bio-optical parameters, and phytoplankton cells enumerations, we assess the feasibility of using multispectral and hyperspectral approaches for detecting spring blooms of Phaeocystis globosa (P. globosa). The two reflectance ratios (Rrs(490) /Rrs(510) and Rrs(442.5) /Rrs(490)), used in the multispectral inversion, suggest that detection of P. globosa blooms are possible from current ocean color sensors. The effects of chlorophyll concentration, colored dissolved organic matter (CDOM), and particulate matter composition on the performance of this multispectral approach are investigated via sensitivity analysis. This analysis indicates that the development of a remote sensing algorithm, based on the values of these two ratios, should include information about CDOM concentration. The hyperspectral inversion is based on the analysis of the second derivative of Rrs(λ) (dλ2 Rrs). Two criteria, based on the position of the maxima and minima of dλ2Rrs, are established to discriminate the P. globosa blooms from diatoms blooms. We show that the position of these extremes is related to the specific absorption spectrum of P. globosa and is significantly correlated with the relative biomass of P. globosa. This result confirms the advantage of a hyperspectral over multispectral inversion for species identification and enumeration from satellite observations of ocean color. Copyright 2008 by the American Geophysical Union.

Atmospheric Corrections and Multi-Conditional Algorithm for Multi-Sensor Remote Sensing of Suspended Particulate Matter in Low-to-High Turbidity Levels Coastal Waters

The accurate measurement of suspended particulate matter (SPM) concentrations in coastal waters is of crucial importance for ecosystem studies, sediment transport monitoring, and assessment of anthropogenic impacts in the coastal ocean. Ocean color remote sensing is an efficient tool to monitor SPM spatio-temporal variability in coastal waters. However, near-shore satellite images are complex to correct for atmospheric effects due to the proximity of land and to the high level of reflectance caused by high SPM concentrations in the visible and near-infrared spectral regions. The water reflectance signal (ρw) tends to saturate at short visible wavelengths when the SPM concentration increases. Using a comprehensive dataset of high-resolution satellite imagery and in situ SPM and water reflectance data, this study presents (i) an assessment of existing atmospheric correction (AC) algorithms developed for turbid coastal waters; and (ii) a switching method that automatically selects the most sensitive SPM vs. ρw relationship, to avoid saturation effects when computing the SPM concentration. The approach is applied to satellite data acquired by three medium-high spatial resolution sensors (Landsat-8/Operational Land Imager, National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite and Aqua/Moderate Resolution Imaging Spectrometer) to map the SPM concentration in some of the most turbid areas of the European coastal ocean, namely the Gironde and Loire estuaries as well as Bourgneuf Bay on the French Atlantic coast. For all three sensors, AC methods based on the use of short-wave infrared (SWIR) spectral bands were tested, and the consistency of the retrieved water reflectance was examined along transects from low- to high-turbidity waters. For OLI data, we also compared a SWIR-based AC (ACOLITE) with a method based on multi-temporal analyses of atmospheric constituents (MACCS). For the selected scenes, the ACOLITE-MACCS difference was lower than 7%. Despite some inaccuracies in ρw retrieval, we demonstrate that the SPM concentration can be reliably estimated using OLI, MODIS and VIIRS, regardless of their differences in spatial and spectral resolutions. Match-ups between the OLI-derived SPM concentration and autonomous field measurements from the Loire and Gironde estuaries’ monitoring networks provided satisfactory results. The multi-sensor approach together with the multi-conditional algorithm presented here can be applied to the latest generation of ocean color sensors (namely Sentinel2/MSI and Sentinel3/OLCI) to study SPM dynamics in the coastal ocean at higher spatial and temporal resolutions.

Toward Sentinel-2 High Resolution Remote Sensing of Suspended Particulate Matter in Very Turbid Waters: SPOT4 (Take5) Experiment in the Loire and Gironde Estuaries

At the end of the SPOT4 mission, a four-month experiment was conducted in 2013 to acquire high spatial (20 m) and high temporal (5 days) resolution satellite data. In addition to the SPOT4 (Take5) dataset, we used several Landsat5, 7, 8 images to document the variations in suspended particulate matter (SPM) concentration in the turbid Gironde and Loire estuaries (France). Satellite-derived SPM concentration was validated using automated in situ turbidity measurements from two monitoring networks. The combination of a multi-temporal atmospheric correction method with a near-infrared to visible reflectance band ratio made it possible to quantify SPM surface concentration in moderately to extremely turbid waters (38–4320 g·m−3), at an accuracy sufficient to detect the maximum turbidity zone (MTZ) in both estuaries. Such a multi-sensor approach can be applied to high spatial resolution satellite archives and to the new ESA Sentinel-2 mission. It offers a promising framework to study the response of estuarine ecosystems to global changes at unprecedented spatio-temporal resolution.

HYPXIM: A new hyperspectral sensor combining science/defence applications

This paper synthesizes the technical requirements made by a group of French scientists and defence users expert in hyperspectral imagery to design a new space borne imaging spectrometer. This project called HYPXIM is currently in phase 0 mission study and two French aerospace companies have proposed solutions that are analysed by the CNES. These technical requirements are converted into at-sensor radiance specifications for each scientific application and the final radiance set used for the instrument design is defined.

Assessment of the decadal morphodynamic evolution of a mixed energy inlet using ocean color remote sensing

A consistent time series of synoptic and high-frequency bathymetric observations is fundamental to improving our understanding and predictive capabilities regarding the morphological behavior of large coastal inlets. Based on satellite observations, an original approach is proposed to characterize the long-term morphological evolution of the Arcachon lagoon inlet and to describe sediment bypassing and breaching mechanisms. The almost 26-year-long remotely sensed data archive used in this study is built from 78 suitable SPOT images (1986–2012) collected in the framework of the KALIDEOS-Littoral program. Bathymetric information is derived from satellite data using a physics-based model. A validation exercise performed on a large bathymetric survey data set (N = 43,949) demonstrates that the inversion model performs excellently in estimating the depth of mildly to moderately turbid shallow waters. The performance of the model suggests that the minimum requirements are fulfilled to apply the SPOT-derived bathymetry to morphodynamic applications. We demonstrate that high-spatial-resolution multispectral sensors are well adapted to analyzing the morphological evolution of small- (i.e., sand dunes), medium- (i.e., sandbanks and channels), and large- (i.e., the entire inlet-lagoon system) scale sedimentary structures present in coastal inlets. For the first time, the long-term evolution of a flood and ebb-tidal delta is characterized by observations at a seasonal timescale. Finally, migration rates of sedimentary entities are quantified, and fundamental mechanisms driving the sediment transport cross the inlet are confirmed.

Hyperspectral field database in support to coastal wetland mapping

This paper presents the hyperspectral field measurements acquired at Arcachon (France) over sediments and vegetation species characteristic of the macrotidal lagoon. The reflectance spectra are analyzed to define the variables of interest in order to propose a classification scheme useful for management purposes. The main results show that coupling winter to summer optical multispectral imagery allows discriminating invasive (Spartina anglica) from endemic (Spartina maritima) lower salt-marsh vegetation species. Also, the bottom color and henceforth sediment mineralogy largely influences the determination of seagrass meadows cover. In addition, preliminary results show the potential of identifying biofilms over sandy to muddy sand. Finally, the characterization of macro-algae deposits on the intertidal flats may involve the exploitation of finer and more numerous spectral bands than those typical of classical multispectral imagery.

Atmospheric Correction of Multi-Spectral Littoral Images Using a PHOTONS/AERONET-Based Regional Aerosol Model

Spatial resolution is the main instrumental requirement for the multi-spectral optical space missions that address the scientific issues of marine coastal systems. This spatial resolution should be at least decametric. Aquatic color data processing associated with these environments requires specific atmospheric corrections (AC) suitable for the spectral characteristics of high spatial resolution sensors (HRS) as well as the high range of atmospheric and marine optical properties. The objective of the present study is to develop and demonstrate the potential of a ground-based AC approach adaptable to any HRS for regional monitoring and security of littoral systems. The in Situ-based Atmospheric CORrection (SACOR) algorithm is based on simulations provided by a Successive Order of Scattering code (SOS), which is constrained by a simple regional aerosol particle model (RAM). This RAM is defined from the mixture of a standard tropospheric and maritime aerosol type. The RAM is derived from the following two processes. The first process involved the analysis of a 6-year data set composed of aerosol optical and microphysical properties acquired through the ground-based PHOTONS/AERONET network located at Arcachon (France). The second process was related to aerosol climatology using the NOAA hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model. Results show that aerosols have a bimodal particle size distribution regardless of the season and are mainly represented by a mixed coastal continental type. Furthermore, the results indicate that aerosols originate from both the Atlantic Ocean (53.6%) and Continental Europe (46.4%). Based on these results, absorbing biomass burning, urban-industrial and desert dust particles have not been considered although they represent on average 19% of the occurrences. This represents the main current limitation of the RAM. An assessment of the performances of SACOR is then performed by inter-comparing the water-leaving reflectance ( ρ w ) retrievals with three different AC methods (ACOLITE, MACCS and 6SV using three different standard aerosol types) using match-ups (N = 8) composed of Landsat-8/Operational Land Imager (OLI) scenes and field radiometric measurements. Results indicate consistency with the SWIR-based ACOLITE method, which shows the best performance, except in the green channel where SACOR matches well with the in-situ data (relative error of 7%). In conclusion, the study demonstrates the high potential of the SACOR approach for the retrieval of ρ w . In the future, the method could be improved by using an adaptive aerosol model, which may select the most relevant local aerosol model following the origin of the atmospheric air mass, and could be applied to the latest HRS (Sentinel-2/MSI, SPOT6-7, Pleiades 1A-1B).